用於遠程長波紅外高光譜成像中之大氣補償的基於集合的Transformer

摘要

被動長波紅外（LWIR）高光譜成像在遠距離幾何構型下，依賴於大氣吸收與發射以及反射輻射亮度，因此需要進行大氣校正才能獲取目標物體的相關資訊。儘管其重要性不容忽視，但由於實際操作與建模上的困難，此校正工作在很大程度上被忽略。本文提出一個輕量級的基於集合的深度學習框架，該框架以在不同遠距離範圍收集的多個輻射亮度測量值為輸入，並聯合估計大氣透過率、大氣路徑輻射以及共同的下行輻射光譜。我們利用稀疏自編碼器分析所學習的特徵表示，並觀察到：儘管缺乏位置監督，部分潛在特徵仍會對測試資料中地理上一致的子集產生活化。在基於MODTRAN生成的遠距離LWIR資料集上的實驗結果表明，所有估計產品的頻譜失真均較低。該資料集與程式碼已公開於：https://factral.co/SAE-LWIR/

English

Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: https://factral.co/SAE-LWIR/